The PR-OCT imaging detected images of colon cancer (top photo) and of normal colon tissue. The green boxes indicate the scores of probability of the predicted “teeth” patterns in the tissue. (Credit: Zhu Lab)

Researchers are developing a new imaging technique that can provide accurate, real-time, computer-aided diagnosis of colorectal cancer. Using deep learning, a type of machine learning, researchers used the technique on more than 26,000 individual frames of imaging data from colorectal tissue samples to determine the method’s accuracy.

Compared with pathology reports, they were able to identify tumors with 100 percent accuracy in this pilot study. This is the first report using this type of imaging combined with machine learning to distinguish healthy colorectal tissue from precancerous polyps and cancerous tissue

The investigational technique is based on optical coherence tomography (OCT), an optical imaging technology that has been used for two decades in ophthalmology to take images of the retina. However, engineers have been advancing the technology for other uses since it provides high spatial and depth resolution for up to 1- to 2-mm imaging depth.

OCT detects the differences in the way health and diseased tissue refract light and is highly sensitive to precancerous and early cancer morphological changes. When further developed, the technique could be used as a real-time, noninvasive imaging tool alongside traditional colonoscopy to assist with screening deeply seated precancerous polyps and early-stage colon cancers.